414 research outputs found
A Model-Based Approach To Requirements Analysis
A major task in designing embedded systems is the systematic
elaboration of functional system requirements and their integration
into the environment of the complete technical system. The main
challenge is to handle the versatile tasks of coordinating a
definition of behavior, which is appropriate to the problem. The
problem- and design-specifications of the customer related product
definition have to be adjusted with and integrated into the manifold
requirements of the technical system design. Accordingly, the
model-based requirements analysis and system-definition presented here defines a well-structured modeling approach,
which systematically aids the goal-oriented formulation and adjustment
of the different stakeholder-requirements with the aid of views onto
the system and descriptive specification techniques. Thus it allows a
clear specification of a consistent and complete system design. The
central steps of this approach are implemented in a requirements management (RM) tool prototype called AutoRAI
Transit Node Routing Reconsidered
Transit Node Routing (TNR) is a fast and exact distance oracle for road
networks. We show several new results for TNR. First, we give a surprisingly
simple implementation fully based on Contraction Hierarchies that speeds up
preprocessing by an order of magnitude approaching the time for just finding a
CH (which alone has two orders of magnitude larger query time). We also develop
a very effective purely graph theoretical locality filter without any
compromise in query times. Finally, we show that a specialization to the online
many-to-one (or one-to-many) shortest path further speeds up query time by an
order of magnitude. This variant even has better query time than the fastest
known previous methods which need much more space.Comment: 19 pages, submitted to SEA'201
Compressed Transmission of Route Descriptions
We present two methods to compress the description of a route in a road
network, i.e., of a path in a directed graph. The first method represents a
path by a sequence of via edges. The subpaths between the via edges have to be
unique shortest paths. Instead of via edges also via nodes can be used, though
this requires some simple preprocessing. The second method uses contraction
hierarchies to replace subpaths of the original path by shortcuts. The two
methods can be combined with each other. Also, we propose the application to
mobile server based routing: We compute the route on a server which has access
to the latest information about congestions for example. Then we transmit the
computed route to the car using some mobile radio communication. There, we
apply the compression to save costs and transmission time. If the compression
works well, we can transmit routes even when the bandwidth is low. Although we
have not evaluated our ideas with realistic data yet, they are quite promising.Comment: 7 pages, technical repor
Advanced Route Planning in Transportation Networks
We present fast and efficient algorithms for routing in road and public transit networks. An algorithm for public transit can handle very large and poorly structured networks in a fully realistic scenario. Algorithms to answer flexible shortest path queries consider additional query parameters, such as edge weight or restrictions. Finally, specialized algorithms compute sets of related shortest path distances for time-dependent distance table computation, ride sharing and closest POI location
Fast Detour Computation for Ride Sharing
Todays ride sharing services still mimic a better billboard. They list the
offers and allow to search for the source and target city, sometimes enriched
with radial search. So finding a connection between big cities is quite easy.
These places are on a list of designated origin and distination points. But
when you want to go from a small town to another small town, even when they are
next to a freeway, you run into problems. You can't find offers that would or
could pass by the town easily with little or no detour. We solve this
interesting problem by presenting a fast algorithm that computes the offers
with the smallest detours w.r.t. a request. Our experiments show that the
problem is efficiently solvable in times suitable for a web service
implementation. For realistic database size we achieve lookup times of about
5ms and a matching rate of 90% instead of just 70% for the simple matching
algorithms used today.Comment: 5 pages, 2 figure environment, 4 includegraphic
Lower Bounds in the Preprocessing and Query Phases of Routing Algorithms
In the last decade, there has been a substantial amount of research in
finding routing algorithms designed specifically to run on real-world graphs.
In 2010, Abraham et al. showed upper bounds on the query time in terms of a
graph's highway dimension and diameter for the current fastest routing
algorithms, including contraction hierarchies, transit node routing, and hub
labeling. In this paper, we show corresponding lower bounds for the same three
algorithms. We also show how to improve a result by Milosavljevic which lower
bounds the number of shortcuts added in the preprocessing stage for contraction
hierarchies. We relax the assumption of an optimal contraction order (which is
NP-hard to compute), allowing the result to be applicable to real-world
instances. Finally, we give a proof that optimal preprocessing for hub labeling
is NP-hard. Hardness of optimal preprocessing is known for most routing
algorithms, and was suspected to be true for hub labeling
Heuristic Contraction Hierarchies with Approximation Guarantee
We present a new heuristic point-to-point shortest path algorithm based on contraction hierarchies (CH). Given an epsilon > 0, we can prove that the length of the path computed by our algorithm is at most (1 + epsilon) times the length of the optimal (shortest) path. Exact CH is based on node contraction: removing nodes from a network and adding shortcuts to preserve shortest path distances. Our heuristic CH tries to avoid adding shortcuts even when a replacement path is (1 + epsilon) times longer. However, we cannot avoid all such shortcuts, as we need to ensure that errors do not stack. Combinations with goal-directed techniques bring further speed-ups
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